Let Big Data connect the dots in your business

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1 Let Big Data connect the dots in your business Big Data Conven-on - September 25, 2014 Golden Tulip Brussels Airport Falke Van Onacker Segment Leader for Big Data Analy4cs IBM SoIware Group Belgium & Luxembourg

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3 Pop up slides 3

4 Pop up slides CuNng- Edge «Freemium» soiware for YOU Go to hvps://www.ibm.com/analy-cs/watson- analy-cs/sign- up/ index.html?source=default&s_cmp=az- neo 4

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6 Many Industries are in the middle of all kinds of Hurricanes Digital disrup-on is now in full bloom at European, Australian newspapers If newspaper companies cannot produce sufficient revenues from digital, if they cannot produce exci4ng, engaging offerings for both readers and adver4sers, they are des4ned to offer mediocre products with nothing to differen=ate them from the mass of faux news. Source: «Poynter» - online ar4cle June 9, 2014 Fiat Chrysler s boss, Sergio Marchionne, is worried that it will cost his company money to provide a venue to host other people s par=es 6 Source: The Economist The Connected Car September 6, 2014 «Meet the Stra=»

7 Automotive & Transport Industries are in the middle of all kinds of Hurricanes 7 Copyright by Boston Consul4ng Group source: hvp://en.wikipedia.org/wiki/growth%e2%80%93share_matrix

8 Real-world, Local, Use Cases 8

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12 ! Operations Analysis"! Only vendor combining at-rest vehicle data with real time datain-use from vehicles for single, integrated view and analysis within and outside of Hadoop environment Predict demand for replacement parts and service Monetize telematics data Provide drivers assistance Advanced Condition Monitoring 12

13 IBM Approach & Reference Architecture 13

14 Introduction IBM Big data architectural overview Paradigm shifts enabled by big data Leverage all data being captured Reduce effort to leverage data Data drive the process In motion data analysis 14

15 Big Data is Changing the Value Equation Analyzing MORE Data will provide MORE Value Cost outweighs value of analyzing more data Jump in value curve from new data sources and types Reduction in incremental costs from new Big Data technologies 15

16 of every big data & analytics project is spent Finding Data Understanding Data Cleansing Data Integrating Data 16

17 Better information through transformation Deliver faster, deeper insights while reducing costs Data types Actionable insight Operational systems Staging area Enterprise Warehouse Predictive analytics and modeling Transaction and application data Data Marts Reporting and analysis Archive 17

18 Better information through transformation Leverage column-store and in-memory capabilities to improve performance and enable reporting & analysis directly against operational data Data types Actionable insight Operational systems Staging area Enterprise Warehouse Predictive analytics and modeling Transaction and application data Reporting & interactive analysis Reporting and analysis Archive 18

19 Better information through transformation Provide dedicated analytics processing for faster, deeper analysis and modeling Data types Actionable insight Operational systems Staging area Enterprise Warehouse Deep analytics & modeling Predictive analytics and modeling Transaction and application data Reporting & interactive analysis Reporting and analysis Archive 19

20 Better information through transformation Leverage Hadoop to capture operational data, leverage additional data types and enable exploration of data prior to normalization Data types Actionable insight Image and video Operational systems Exploration and landing Trusted data Deep analytics & modeling Predictive analytics and modeling Enterprise content Transaction and application data Reporting & interactive analysis Reporting, analysis, content analytics Social data Third-party data Archive Discovery and exploration 20

21 Better information through transformation Leverage Hadoop for queryable archive Data types Actionable insight Image and video Operational systems Exploration, landing and archive Trusted data Deep analytics & modeling Predictive analytics and modeling Enterprise content Transaction and application data Reporting & interactive analysis Reporting, analysis, content analytics Social data Third-party data Archive Discovery and exploration 21

22 Better information through transformation Leverage data in motion and streamline processing of extreme volumes Data types Real-time processing & analytics Actionable insight Machine and sensor data Image and video Operational systems Exploration, landing and archive Trusted data Deep analytics & modeling Decision management Predictive analytics and modeling Enterprise content Transaction and application data Reporting & interactive analysis Reporting, analysis, content analytics Social data Third-party data Discovery and exploration 22

23 Better information through transformation Extend transformation, matching, security and governance capabilities to ALL data Data types Real-time processing & analytics Actionable insight Machine and sensor data Image and video Operational systems Exploration, landing and archive Trusted data Deep analytics & modeling Decision management Predictive analytics and modeling Enterprise content Transaction and application data Reporting & interactive analysis Reporting, analysis, content analytics Social data Third-party data Discovery and exploration Information Integration Data Matching & MDM Metadata & Lineage Security & Privacy Information Integration & Governance Lifecycle Management 23

24 Next generation architecture for delivering information and insights Data types Real-time processing & analytics Actionable insight Machine and sensor data Image and video Operational systems Exploration, landing and archive Trusted data Deep analytics & modeling Decision management Predictive analytics and modeling Enterprise content Transaction and application data Reporting & interactive analysis Reporting, analysis, content analytics Social data Third-party data Discovery and exploration Information Integration & Governance 24

25 The Logical Data Warehouse Leverage fit for purpose components and zones Data types Real-time processing & analytics Actionable insight Machine and sensor data Image and video Operational systems Exploration, landing and archive Trusted data Deep analytics & modeling Decision management Predictive analytics and modeling Enterprise content Transaction and application data Reporting & interactive analysis Reporting, analysis, content analytics Social data Third-party data Logical Data Warehouse Discovery and exploration Information Integration & Governance 25

26 IBM Big Data & Analytics offerings Data types Real-time processing & analytics INFOSPHERE STREAMS Actionable insight Machine and sensor data Image and video Enterprise content Transaction and application data Social data Operational systems DB2, INFORMIX PUREDATA TRANSACTIONS Exploration, landing and archive Trusted data PUREDATA ANALYTICS Deep analytics & modeling PUREDATA ANALYTICS Reporting & interactive analysis PUREDATA ANALYTICS Decision management SPSS MODELER GOLD Predictive analytics and modeling SPSS MODELER Reporting, analysis, content analytics COGNOS BI COGNOS TM1 Third-party data INFOSPHERE BIG INSIGHTS Discovery and exploration WATSON EXPLORER Information Integration & Governance INFORMATION SERVER, MDM, G2, GUARDIUM, OPTIM 26

27 IBM Differentiators More than Hadoop Greater resiliency and recoverability Advanced workload management & multi-tenancy Enhanced, flexible storage management (GPFS) Enhanced data access (BigSQL, Search) Analytics accelerators & visualization Enterprise-ready security framework Data in Motion Enterprise class stream processing & analytics Analytics Everywhere Richest set of analytics capabilities Ability to analyze data in place Governance Everywhere Complete integration & governance capabilities Ability to govern all data where ever it is Complete Portfolio End-to-end capabilities to address all needs Ability to grow and address future needs Remains open to work with existing investments 27

28 IBM Client Value Engagement Getting started 28

29 CVE for Big Data Analytics: Methodology Define & Identify technical & business Problems / Challenges Identify Future Process & Costs with the Recommended Solution (To-Be) CVE Engagement Summary & Final Analysis Identify Technical & Business Initiatives Determine Current State Process & Costs (As-Is) Determine Future State, Process & Costs (To- Be) Technical Solution Blueprint CVE Final Results + Definition of Scope for PoC Identify Current State, Process & Related Costs (As-Is) Measure the Difference Between As-Is & To-Be 29

30 CVE Roles: Client Participation Required Client Roles Client Executive Sponsor Role Description Details top client organizational priorities Provides high-level view of top organizational challenges Supplies key decision-making criteria (In scope solution) Client CVE Coordinator Responsible for scheduling interviews, Resolves and/or elevating any client process issues Helps to facilitate any challenges during the CVE process Communicates the Executive Sponsor s top priorities Client Interview Roles: CVE Offering: Time to Value CVE Offering: Reduced Infra. Costs CVE Offering: Value of New Data Sources Business Level Discussions Business Analysts Depending on Analysis Business Area (Representatives from) Marketing Finance Sales Security Accounting IT Level Discussions CIO, Enterprise Architects, Application Managers: Database Administrator & System Administrator Analytic Modelers, BI Developers, Report Writers ETL and DW Developers 30 Average client resource time is 2-4 hours

31 Big Data Analytics CVE: Timeline Time estimates are adjustable based on client time lines & priorities The average CVE is completed in 2-3 weeks from start to finish Average client resource time is 2-4 hours CVE requires minimal client resource time 4 primary CVE touch points: Introduction, Interview, Validation and Final Presentation Tasks Week 1 Week 2 Week 3 Week 4 Client Introduction Develop CVE Charter Conduct CVE Interviews Validate CVE Analysis + Define PoC Scope Final Presentation 31

32 QUESTIONS? Falke Van Onacker Segment Leader for Big Data Analy4cs IBM Belgium & Luxembourg Mobile: 0032/

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